Let’s say you’ve been travelling across Peninsular Malaysia looking for a particular animal or plant over the years and you’ve marked the GPS coordinates of its presence through an indirect sign (e.g., tracks or vocalizations) or an actual sighting. And one day, you decide to make a map of its distribution, but of course, you do not have the time and effort to look in every nook and cranny of the peninsula to make an accurate map. So wouldn’t you like to know potential places where your species might be found? Over the years, scientists have developed a range of species distribution models (SDMs) to help you do just that. SDMs try to establish a relationship between your species records and the environmental or spatial characteristics (e.g., rainfall, temperature, forest cover, land use types, distance to water sources) of your sampling area (Franklin 2009). In other words, SDMs help to predict where you might find other suitable habitats for your species – you don’t always have to depend on luck to go find them! One of the more popular types of SDMs is Maximum Entropy Modelling (see Phillips et al. 2006; Phillips and Dudík 2008). We won’t go through the statistical explanations for MaxEnt modelling, but you can refer to Elith et al. (2011). However, one limitation of presence-only data is the effect of sample selection bias, where some areas in the landscape are sampled more intensively than others (Phillips et al. 2009) – MaxEnt requires an unbiased sample and this applies to all SDMs. Two of Rimba’s researchers, Reuben and Sheema, were recently involved in a study on tapirs. In this study, some areas in Peninsular Malaysia were more intensively surveyed for tapirs than others and the predictions by MaxEnt were less biologically meaningful if this bias was not accounted for. So a bias grid was created to upweight presence-only datapoints with fewer neighbours in the geographic landscape. You can now create bias grids in R. Here’s a tutorial on how to do so.

References

Elith J, Phillips SJ, Hastie T, Dudík M, Chee YE, Yates CJ (2011) A statistical explanation of MaxEnt for ecologists. Diversity and Distributions 17:43-57 Franklin, J. (2009) Mapping species distributions: spatial inference and prediction. Cambridge University Press, Cambridge, UK. Phillips SJ, Anderson RP, Schapire RE (2006) Maximum entropy modelling of species geographic distributions. Ecological Modelling 190:231-259.

Phillips SJ, Dudík M (2008) Modeling of species distributions with MaxEnt: new extensions and a comprehensive evaluation. Ecography 31:161-175. Phillips SJ, Dudík M, Elith J, Graham CH, Lehmann A, Leathwick J, Ferrier S (2009) Sample selection bias and presence-only distribution models: implications for background and pseudo-absence data. Ecological Applications 19:181-197.